Software Engineer - II, AI/ML

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Role Overview As a Software Engineer II, AI/ML, you will design, build, and deploy Generative AI and machine learning solutions that power enterprise applications — from intelligent assistants and RAG-based systems to agentic workflow automation — working across the full AI pipeline from data ingestion to production inference. Key Responsibilities Build Generative AI features for enterprise applications, including text generation, summarization, and RAG-based intelligent assistants Design and orchestrate multi-agent AI systems that automate complex workflows and decision-making Design, train, and deploy machine learning models to solve classification, regression, NLP, and other business problems Own AI pipelines end-to-end, from data ingestion through model inference and serving Evaluate and monitor model and agent performance in production, iterating based on results Write clean, scalable, production-ready code and maintain source control and CI/CD workflows Partner with cross-functional teams in an Agile/Scrum environment to ship AI capabilities Desired Experience Generative AI & LLM Applications Experience developing Generative AI solutions using large language models (LLMs) for enterprise use cases such as text generation, summarization, and Retrieval-Augmented Generation (RAG) Experience building intelligent assistants and conversational AI applications powered by LLMs Familiarity with vector databases for RAG retrieval, such as AWS OpenSearch, pgVector, or Databricks AI Search Agentic AI & Orchestration Experience implementing Agentic AI systems using multi-agent frameworks to automate complex workflows and decision-making pipelines Hands-on experience with LangChain and LangGraph for orchestration and agentic workflow development Experience using Pydantic AI for structured data validation and AI model integration Machine Learning Experience designing, building, and deploying machine learning models for classification, regression, NLP, and other AI use cases Hands-on experience with Scikit-learn, NumPy, Pandas, PyTorch, and TensorFlow/Keras AI Pipelines & Evaluation Experience building and maintaining AI pipelines spanning data ingestion, model inference, and serving Familiarity with evaluation and observability tools such as LangSmith, MLflow, or Weights & Biases Familiarity with LLM platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI Software Engineering & Tools